Thermographic Detection of Internal Passageway Blockages

ABSTRACT

A method of thermal inspection of a component defining at least one internal passageway. The method includes receiving a continuous sequence of thermal images of at least an exit hole defined by the at least one internal passageway at a surface of the component. The method also includes delivering a pressurized airflow pulse into the at least one internal passageway, receiving a temperature response signal as function of time based on the received thermal images, determining a first derivative of the temperature response signal, and determining a level of blockage of the at least one internal passageway based on the first derivative of the temperature response signal.

CROSS REFERENCE TO RELATED APPLICATIONS

This U.S. patent application claims priority under 35 U.S.C. §119(e) to U.S. Provisional Application 61/314,848, filed on Mar. 17, 2010, which is hereby incorporated by reference in its entirety.

TECHNICAL FIELD

This disclosure relates to thermographic detection of internal passageway blockages of an object.

BACKGROUND

For improved thermodynamic efficiency of a gas turbine engine, a high pressure turbine section of the engine may be operated at its highest possible temperature. However, the operating temperature of the turbine increases, so do thermal stresses on turbine airfoil components, such as blades and vanes. These stresses may result in reduced lifetime of the components and potential failure during operation. Modern aircraft turbine engines, as well as ground-based turbines for power generation, have addressed the seemingly divergent requirements for higher operating temperatures and protection and preservation of the turbine airfoils with significant advances in design, materials and manufacturing technologies.

Modern turbines airfoils are generally constructed of metallic superalloys, which provide superior mechanical strength, resistance to creep, oxidation and corrosion, and long fatigue lifetimes at high temperatures compared to conventional metals and alloys. High temperature performance can be further enhanced by a network of internal cooling channels that allow circulation of a cooling fluid (typically air) through the airfoil. Exhaust ducts and holes in the airfoil surface expel air from the internal channels. The holes may be designed to direct a film of cooling fluid along the exterior surface of the airfoil to further enhance cooling.

Various method of using infrared thermography can be used to detect blockages in holes of a component, such as cooling holes of an airfoil. Generally, the methods typically involve pumping a fluid (either hot, cold or alternating hot and cold) through the component and then viewing the resulting infrared image of a surface of the component to determine if the fluid is properly expelled from all holes of the component. In some cases, the gas is chosen to be visible in the infrared spectrum. In other methods, the input and exhaust pressures of the gas are closely monitored with sensors, and deviations in the relationship between these pressures is taken to be an indication of blockage. These methods are generally successful in detecting complete, or near complete blockages, but they are often unable to detect partial blockages.

SUMMARY

In order to operate in high temperature environments that exist in airborne and land based turbines, components such as blades and vanes are typically cooled by a fluid (e.g., air or steam) that flows through an array of internal channels, and is expelled through small exhaust holes on the component surface. Blockage of either a channel or hole during turbine operation results in elevated local temperatures that could cause a critical failure of the component. The present disclosure provides methods for detecting these blockages and other imperfections, defects, and/or features of an analyzed object, such as an airfoil (e.g., a turbine blade or vane).

At present, methods for detecting these blockages are crude and time consuming. For example, a common inspection method requires that an inspector interrogate each hole manually with a wire. Another method involves pumping a fluid through the component, and qualitatively observing the stream flowing from each hole. These methods are imprecise, and often unable to detect partial blockages.

One aspect of the disclosure provides a method of thermal inspection of a component defining at least one internal passageway. The method includes receiving a continuous sequence of thermal images of at least an exit hole defined by the at least one internal passageway at a surface of the component. The method also includes delivering a pressurized airflow pulse (e.g., room temperature air) into the at least one internal passageway, receiving a temperature response signal as function of time based on the received thermal images, determining a first derivative of the temperature response signal, and determining a level of blockage of the at least one internal passageway based on the first derivative of the temperature response signal.

Implementations of the disclosure may include one or more of the following features. Although the method includes delivering a pulse of air, the method can also be performed by continuously delivering a flow of air to the component and modulating the air flow delivery, such as by using a step function or other pulsing effect. In some implementations, the method includes acquiring the thermal images at a frame rate of at least 150 Hz. The method may include comparing the first derivative of the temperature response signal of the tested component with a first derivative of a temperature response signal for a reference component having an unblocked corresponding at least one internal passageway. In some examples, the method includes evaluating a pixel time history of the received thermal images to identify an airflow onset time period and an airflow shut-off time period. Peaks of the first derivative of the temperature response signal may be identified to determine the onset time period and the shut-off time period. The method may include identifying image pixels having a positive derivative peak immediately followed in time by a negative derivative peak as corresponding to the onset time period. Moreover, the method may include identifying image pixels having a negative derivative peak immediately followed in time by a positive derivative peak as corresponding to the shut-off time period.

In some implementations, the method includes identifying image pixels having a positive derivative peak immediately followed in time by a negative derivative peak, where both derivative peaks have an amplitude greater than a first threshold peak value. The method may also include identifying image pixels having a negative derivative peak immediately followed in time by a positive derivative peak, where both derivative peaks have an amplitude greater than a second threshold peak value. The first and second threshold peak values can be equal. In some examples, the method includes identifying image pixels wherein a difference between the identified positive derivative peaks and the negative derivative peaks for the onset and shut-off time periods is greater than a third threshold value. To determine that the test component returns to an equilibrium state, the method may include identifying image pixels having a first derivative of the temperature response signal that returns to within an equilibrium range of zero within a threshold number of image acquisition frames.

The method, in some implementations, includes associating an unblocked blockage level with image pixels evaluated as having 1) a positive derivative peak immediately followed in time by a negative derivative peak, where both derivative peaks have an amplitude greater than a first threshold peak value, 2) a negative derivative peak immediately followed in time by a positive derivative peak, where both derivative peaks have an amplitude greater than a second threshold peak value, and 3) a difference between the identified positive derivative peaks and the negative derivative peaks for the onset and shut-off time periods is greater than a third threshold value. The first and second threshold peak values can be equal or different. Moreover, the threshold peak values may be determined by evaluating a statistically significant number of components (e.g., air foils) that have been determined to be blocked or unblocked by other methods.

An image corresponding to the tested component can be electronically displayed. Pixels of the image may indicate a level of passageway blockage (e.g., based on derivative amplitude). The image may be a thermal image of the tested component.

The method may include delivering a sequence of separate discrete pressurized airflow pulses into the at least one internal passageway. A portion of the temperature response signal corresponding to a first air pulse of the air pulse sequence may be ignored (e.g., because the component may move to become seated in a test fixture upon receiving the first air pulse).

Another aspect of the disclosure provides a method of thermal inspection of a component defining at least one internal passageway. The method includes receiving a continuous sequence of thermal images of at least an exit hole defined by the at least one internal passageway at a surface of the component and delivering a pressurized airflow pulse into the at least one internal passageway. The exit hole is arranged or constructed to expel a film of air across a surface of the component to enhance convective cooling of the surface. The method further includes determining a shape of the air film based on at least one thermal image, and comparing the determined air film shape to a reference shape to determine whether the exit hole meets a specification (e.g., dimensional, performance, etc.). In some implementations, the method includes receiving a temperature response signal as function of time for each corresponding pixel of the received thermal images and determining the shape of the air film based on the temperature response signals.

In yet another aspect, a method of thermal inspection of a component defining at least one internal passageway includes receiving a continuous sequence of infrared images of a surface of the component, delivering a pressurized airflow pulse into the at least one internal passageway, and optionally ceasing delivery of the airflow pulse. The method also includes receiving a temperature response signal as function of time based on the received thermal images, receiving a thermal diffusivity of the component, fitting a mathematical expression to the received temperature response signal, and determining a wall thickness of the component.

In some implementations, the mathematical expression comprises an expression for one-dimensional diffusion through a plate subjected to heating with a plate thickness as a free parameter. The method may include fitting the mathematical expression to a monotonically rising portion of the temperature response signal. The thermal images may be acquired at a frame rate of at least 150 Hz.

The method may include determining a temperature response signal for each corresponding pixel of the received thermal images. Moreover, the method may include identifying a location on the thermal images of the at least one internal passageway and determining a wall thickness between the at least one internal passageway and a surface of the component.

Another aspect of the disclosure provides yet another method of thermal inspection of a component defining at least one internal passageway. The method includes receiving a continuous sequence of infrared images of a surface of the component, delivering a pressurized airflow pulse into the at least one internal passageway, and optionally ceasing delivery of the airflow pulse. The method also includes receiving a temperature response signal as function of time based on the received thermal images and executing thermographic signal reconstruction on a monotonically rising portion of the temperature response signal occurring during a time interval starting at an onset of the air pulse delivery and ending at a time when the temperature response signal attains a maximum temperature. The method includes comparing a first derivative of the reconstructed temperature response signal with a first derivative of a reconstructed reference temperature response signal to determine if the component meets a specification. The method may also or alternatively include comparing a second derivative of the reconstructed temperature response signal with a second derivative of a reconstructed reference temperature response signal to determine if the component meets a specification.

In some implementations, the thermographic signal reconstruction includes subtracting a pre-excitation signal portion from the temperature response signal, smoothing the temperature response signal, scaling the smoothed temperature response signal, interpolating between raw data points used for determining the temperature response signal, fitting a low order polynomial to the interpolated data, and executing inverted scaling of the data to provide a reconstruct data set. The method may include determining the first derivative of the reconstructed temperature response signal. In some examples, the method includes identifying a location of the at least one internal passageway on at least one infrared image and comparing the first derivative of the reconstructed temperature response signal at one or more points coinciding with the identified location of the at least one internal passageway with the first derivative of a reconstructed reference temperature response signal to determine if the component meets a specification. In additional examples, the method includes identifying a location of the at least one internal passageway on at least one infrared image and comparing a second derivative of the reconstructed temperature response signal at one or more points coinciding with the identified location of the at least one internal passageway with a second derivative of a reconstructed reference temperature response signal to determine if the component meets a specification.

In another aspect, a method of thermal inspection of a component defining at least one internal passageway includes receiving a continuous sequence of infrared images of a surface of the component, delivering a pressurized airflow pulse into the at least one internal passageway, and receiving a temperature response signal as function of time based on the received thermal images. The method also includes identifying a monotonically rising portion of the temperature response signal occurring during a time interval starting at an onset of the air pulse delivery and ending at a time when the temperature response signal attains a maximum temperature and identifying a monotonically falling portion of the temperature response signal occurring during a time interval starting at cessation of the air pulse delivery and ending at a time when the temperature response signal reaches a minimum temperature.

In some implementations, the method includes fitting a polynomial expression to at least one of the identified monotonical portions of the temperature response signal and/or comprising comparing the temperature response signal to a reference signal corresponding to a validated component.

In yet another aspect, a thermographic testing system includes a pressurized air source configured to receive fluid communication with a component having at least one internal passageway, an infrared camera arranged to capture images of the received component, and a computing device in communication with the infrared camera. The computing device receives a continuous sequence of thermal images of at least an exit hole defined by the at least one internal passageway at a surface of the component. The computing device also causes the air source to deliver a pressurized airflow pulse into the at least one internal passageway of the component and to optionally cease delivery of the airflow pulse. The computing device determines a temperature response signal as function of time based on the received thermal images, a first derivative of the temperature response signal, and a level of blockage of the at least one internal passageway based on the first derivative of the temperature response signal.

In some implementations, the infrared camera operates with a frame rate of at least 150 Hz. The computing device compares the first derivative of the temperature response signal of the tested component with a first derivative of a temperature response signal for a reference component having an unblocked corresponding at least one internal passageway. In some examples, the computing device evaluates a pixel time history of the received thermal images to identify an airflow onset time period and an airflow shut-off time period. The computing device may identify peaks of the first derivative of the temperature response signal to determine the onset time period and the shut-off time period. For example, the computing device can identify image pixels having a positive derivative peak immediately followed in time by a negative derivative peak as corresponding to the onset time period. Moreover, the computing device may identify image pixels having a negative derivative peak immediately followed in time by a positive derivative peak as corresponding to the shut-off time period.

In some implementations, the computing device identifies image pixels having a positive derivative peak immediately followed in time by a negative derivative peak, where both derivative peaks have an amplitude greater than a first threshold peak value. The computing device may also identify image pixels having a negative derivative peak immediately followed in time by a positive derivative peak, where both derivative peaks have an amplitude greater than a second threshold peak value. The first and second threshold peak values can be equal. In some examples, the computing device identifies image pixels wherein a difference between the identified positive derivative peaks and the negative derivative peaks for the onset and shut-off time periods is greater than a third threshold value. The computing device may identify image pixels having a first derivative of the temperature response signal that returns to within an equilibrium range of zero within a threshold number of image acquisition frames.

The computing device may associate a blockage level of unblocked with image pixels evaluated as having 1) a positive derivative peak immediately followed in time by a negative derivative peak, where both derivative peaks have an amplitude greater than a first threshold peak value, 2) a negative derivative peak immediately followed in time by a positive derivative peak, where both derivative peaks have an amplitude greater than a second threshold peak value, and 3) a difference between the identified positive derivative peaks and the negative derivative peaks for the onset and shut-off time periods is greater than a third threshold value. The computing device may electronically display an image corresponding to the tested component (e.g., on a monitor or display in communication with the computing device). Pixels of the image can indicate a level of passageway blockage (e.g., by color coding, etc.). Moreover, the image can be a thermal image of the tested component.

The details of one or more implementations of the disclosure are set forth in the accompanying drawings and the description below. Other aspects, features, and advantages will be apparent from the description and drawings, and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a schematic view of a turbine airfoil manufacturing process.

FIGS. 2 and 3 are schematic views of quality assurance testing procedures for a turbine airfoil manufacturing process.

FIG. 4 is a schematic view of a pin/wire test for checking blockage of a passageway of an exemplary turbine airfoil.

FIG. 5 is a schematic view of a water test for checking blockage of a passageway of an exemplary turbine airfoil.

FIG. 6 is a section view of an exemplary turbine airfoil wall.

FIG. 7 is a schematic view of thermographic testing system for detecting features and/or blocked internal passageways of an object.

FIGS. 8A and 8B provide an exemplary arrangement of operations for thermographically testing an object.

FIG. 9A is a schematic view of an exemplary turbine airfoil in equilibrium with its surrounding environment.

FIG. 9B is a schematic view of the turbine airfoil shown in FIG. 9A initially receiving a pressurized airflow.

FIG. 9C is a schematic view of the turbine airfoil experiencing internal heating due to a rapid compression of previously static air in internal passageways of the turbine airfoil.

FIG. 9D is a schematic view of the compressed air within the turbine airfoil shown in FIG. 9C escaping through exit passageways defined by the airfoil.

FIG. 9E is a schematic view of air expanding in the turbine airfoil shown in FIG. 9D after cessation of delivery of the pressurized air.

FIGS. 10A and 10B are graphical views of exemplary temperature response signals for an unblocked passageway or hole.

FIG. 10C is a graphical view of exemplary temperature response signals for an unblocked hole and a blocked hole.

FIG. 10D is a side view of an image of an exemplary turbine airfoil having marked identified blocked holes.

FIG. 11 is a section view of an exemplary turbine airfoil illustrating air flow paths out of different types of holes.

FIG. 12 is a section view of an exemplary turbine airfoil illustrating how different camera angles can obtain thermal images of internal airfoil surfaces.

FIG. 13 is a section view of an exemplary turbine airfoil experiencing heat conduction from heated internal air through walls of the airfoil.

FIG. 14 is a section view of an exemplary turbine airfoil having an external surface film that experiences film heating due to heated air exiting though an angled exit passageway.

FIG. 15A is a section view illustrating different heat transfer paths through an exemplary turbine airfoil.

FIG. 15B is graphical view of temperature response signals corresponding to the heat transfer paths shown in FIG. 15A.

FIG. 16A is a schematic view illustrating an exemplary film heating path from an airfoil hole over an external surface of an airfoil.

FIG. 16B is a section view of an exemplary turbine airfoil illustrating an airflow exit path from an airfoil hole corresponding to the film heating path shown in FIG. 16A.

FIG. 17A is graphical view of an exemplary temperature response signal of a thermographic testing system for multiple sequential air pulses into a turbine airfoil.

FIG. 17B is graphical view of a first derivative of the temperature response signal shown in FIG. 17A.

FIG. 18A is graphical view of exemplary temperature response signals of a thermographically tested turbine airfoils having a blocked hole, an unblocked hole, and a solid surface.

FIG. 18B is a graphical view of a portion of the temperature response signals shown in FIG. 18A illustrating a monotonically rising signal portion for the temperature response signal of the unblocked hole.

FIG. 19 provides an exemplary arrangement of operations for determining a local wall thickness of an object having internal channels.

FIG. 20 provides an exemplary arrangement of operations for thermographic signal reconstruction (TSR).

FIGS. 21A and 21B are thermal decay graphs illustrating a temperature-time decay characteristic of an imaged sample in a linear domain (FIG. 21A) and in a logarithmic domain (FIG. 21B).

FIG. 22A is an image (formed from reconstructed data) of a front view of a control sample, wherein the control sample contains a plurality of flat bottom holes drilled from the back of the sample at various depths.

FIG. 22B is an image (formed from raw data, i.e. data that has not been conditioned using the reconstruction techniques of the present invention) of a front view of a control sample.

FIGS. 22C and 22D are images created by respectively taking the first and second derivative of the reconstructed data used to form the image of FIG. 22A.

FIG. 23 provides a combined schematic-graphical view of detecting internal passageway debris of an object using path segments applied to a thermal image of the object.

FIG. 24 provides an exemplary arrangement of operations for detecting internal passageway debris of an object.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

Component manufacturing may entail many steps, some of which may include quality inspections. Although the present disclosure describes various methods and apparati for turbine airfoil manufacturing and inspection, these methods and apparati may be used on other types of components as well, and in subsequent inspections after the component has been in service.

Referring to FIGS. 1-3, the manufacture of a turbine airfoil 100 may include several steps, such as a casting, machining, and coating the airfoil 100. The casting process may include pouring a molten superalloy into a mold cavity having ceramic cores that define an internal cooling channel network. After casting the airfoil 100, the ceramic cores can be removed by a chemical leaching process. Multiple inspections can be performed at each manufacturing step or operation. For example, after casting the airfoil 100, a quality inspection may include measuring structural features of the airfoil 100, such as an overall size and/or wall thicknesses, proper formation and placement of internal channels and structures, and checking for cracks or defects. The machining operation may entail forming various features into the airfoil 100, such as cooling channels, passageways, and/or holes. After the machining operation, the airfoil 100 can be inspected for feature size tolerances and/or at least partial blockage of the channels, passageways, or holes. During the coating operation, a thermal barrier coating (TBC) may be applied to the airfoil to enhance performance. After the coating operation, the airfoil 100 may be inspected for adhesion of the coating (e.g., de-lamination), a coating thickness, and/or at least partial blockage of the channels, vanes, or holes.

A number of problems that affect airfoil operational performance may occur during the manufacturing process. For example, residue or debris from the ceramic cores used in casting may remain after the leaching process and create blockages in internal cooling channels. Moreover, the ceramic cores may shift position during the casting process, so that the cast airfoil 100 does not conform to a specification. Other problems may include incomplete machining of cooling holes that results in complete or partial blockage of the hole, compromising cooling performance. Holes may be improperly positioned or drilled at angles outside of the specification. In laser machining, the diameter of a hole may not be consistent through an airfoil wall. Moreover, debris created during the machining process may enter internal cooling passages and create blockages. The coating process may obstruct cooling holes or create debris that enters the internal cooling passageways and creates blockages.

Inspections at each manufacturing operation may include different inspection methods and equipment for analyzing various aspects of the airfoil 100 or for determining various quality metrics. These inspection methods may include ultrasonic testing (UT), X-ray, neutron radiography (N-ray), liquid penetrant inspections, flowing of water therethrough, borescope, pin/wire hole inspection, eddy current analysis, and infrared thermography. For example, after the casting operation, infrared thermography, ultrasonic testing, and/or X-ray testing can be used to determine the structural integrity (e.g., features sizes, wall thicknesses, etc.) of the airfoil 100. After the machining operation, infrared thermography, liquid penetrant inspections, flowing of water therethrough, borescope, and/or pin/wire hole inspections can be used to determine if the airfoil 100 has any cracks or blocked passageways. FIG. 4 illustrates manual insertion of a wire 200 into an exit passageway 120 of the airfoil 100 for determining whether the exit passageway 120 is blocked. The manual wire inspection can be time consuming for large quantities of exit passageways 120, subjective for each operator, and cannot detect partial blockages in complex paths of internal passageways. FIG. 5 illustrates flowing water into an inlet passageway 110 of the airfoil 110, which is in fluid communication with one or more exit passageways 120. A blocked cooling passageway can be detected by a relative flow rate out of each exit passageway 120; however, the method can be subjective and small blockages can be difficult to detect. After the coating operation, infrared thermography and/or ultrasonic testing (UT) can be used to determine an adhesion quality (e.g., by identifying any areas of de-lamination). Moreover, eddy current testing may be used to determine a coating thickness on the airfoil 100.

FIG. 6 provides a section view of a portion of an exemplary airfoil 100 having an inlet passageway 110 in fluid communication with exit passageways 120. In the example shown, debris 300 can accumulate on walls of the passageways 110, 120, decreasing a cross-sectional flow area, and in some instances, entirely blocking one or more of the passageways 110, 120. The debris 300 may enter the airfoil 100 from any number of sources, such as machining debris, coating material, foreign objects in coolant flowed therethrough, residual core from the casting process, etc. A method of infrared thermography may be used to inspect the airfoil 100 after each manufacturing step as well as during maintenance of the airfoil while in commercial operation.

A method of infrared thermographic inspection can be used to detect blockages in airfoil cooling holes and residual core debris in internal channels as well as confirmation of proper operation of film cooling over an airfoil surface. Film cooling may occur when internal passageways and exit holes are unblocked and exit holes are oriented to expel air over the airfoil surface. The inspection method does not require extensive airfoil preparation before testing or air temperature conditioning during inspection. Moreover, the inspection method allows inspection of the entire air foil 100 in a period of a few seconds. While aspects of the inspection method can be enhanced by using a reference airfoil having verified unblocked internal passageways and exit holes, the inspection method can identify blockages using other a priori information and/or based on simple indicators in resultant data.

Referring to FIG. 7, in some implementations, a system 700 for thermographically testing a component 705 having at least one internal passageway, such as the turbine airfoil 100, includes an infrared camera 710, a computing device 720 (e.g., a device having a processor and/or memory) in communication with the infrared camera 710, and a compressed air source 730 configured to receive fluid communication with the component. A valve 740 in communication with the computing device 720 may control a flow of air (e.g., at room temperature or any temperature) from the compressed air source 730 to the component 705. For example, the computing device 720 can control a pressure level of the air flow delivery, a pulse duration, pulse sequence, etc. by controlling the valve 740. The infrared camera 710 is arranged to monitor at least a portion of the component 705. In the example shown, the infrared camera 710 is arranged to view the entire airfoil 100 as the component 705 from one perspective. Mirrors can be used to view multiple sides of the component 705. The infrared camera 710 provides at least one thermal image 712 comprised of pixels 714 (e.g., a sequence of thermal images 712). The computing device 720 receives imaging signals from the infrared camera 710 and may determine a temperature response signal (temperature as a function of time) for each pixel 714. For example, the computing device 720 can have digital image acquisition or analog frame-grabbing capabilities to convert signals or data received from the infrared camera 710 to a format that can be analyzed and mathematically manipulated by the computing device 720. The computing device 720 does not necessarily need to be separate from the camera 710 and that the functions in the computing device 720 can be incorporated into the camera 710 itself as, for example, an on-board integrated circuit. Moreover, the computing device 720 may include an optional acquisition module for generating a complete mosaic image of an imaged sample when the camera 710 obtains multiple spatially different images 712, particularly when the sample is too large to fit in a single image frame.

FIGS. 8A and 8B provides an exemplary arrangement 800 of operations for thermographically testing a component at least one internal passageway. In general, the method includes delivering a brief pulse of a gas (e.g., air or other suitable gas) at room temperature or any other stable temperature through the component to detect blocked internal passageways. Unlike most forced air approaches, the method may neither depend on, nor require any information about the input or output air temperature. Instead, the method may include monitoring the dynamic temperature response of the component as the airflow is first introduced and then shut off.

In the examples shown in FIGS. 9A-9E, the thermographic testing operations are applied to the turbine airfoil 100. One or more of the testing operations can be performed or controlled by the computing device 720. The system 700 can detect the near-instantaneous heating and cooling temperature responses in an immediate vicinity of an unblocked hole 122 of an exit passageway 120 to automatically detect blocked and unblocked holes 122. With additional reference to FIG. 7, the operations include commencing 802 image acquisition by the infrared camera 710. In an initial state, as shown in FIG. 9A, the airfoil 100 receives no air flow from the system 700 (e.g., from an air delivery line 750 in communication with the valve 740) and the airfoil 100 is in thermal equilibrium with its surrounding environment. The infrared camera 710 may collect a continuous sequence of digital images 712 the airfoil 100. The camera may operate at a frame rate fast enough to sample a transient ascending and descending temperature responses of the airfoil 100. In some implementations, the camera operates at a frame rates of at least 150 Hz, and in some examples at about 300 Hz. While higher rates are acceptable, results become progressively degraded at frequencies less than 150 Hz.

Referring to FIGS. 8A and 9B-9D, the operations include delivering 804 a forced air pulse or pressurized airflow into the airfoil 100. The air fills the inlet and exit passageways 110, 120 of the airfoil 100. Substantially immediately after the onset of air flow into the airfoil 100, static air in the airfoil 100 and the delivery line 750 compresses and causes a temperature increase of the internal surfaces of the inlet and exit passageways 110, 120 of the airfoil 100, as illustrated in FIG. 9C. The duration of the compression heating is typically quite brief (e.g., on the order of tens of milliseconds), and determined by the volume of static air in the airfoil 100 and delivery line 750, the number, size and distribution of passageways 110, 120 and exit holes 122, and the pressure of the incoming air. The duration of the compression heating can be adjusted by changing a length or diameter of the delivery line 750. Although the operations include delivering a pulse of air, the inspection can also be performed by continuously delivering a flow of air to the air foil 100 and modulating the air flow delivery, such as by using a step function or other pulsing effect. The compressed air escapes through the exit passageways 120, as illustrated in FIG. 9D.

The sudden introduction of a relatively high pressure gas stream into the fixed volume of the passageways 110, 120 of the airfoil 100, which is in equilibrium with its environment at room temperature, causes substantially instantaneous compression of the static air initially present in the passageways 110, 120, and thus heating of the air. As that heated volume of air is expelled through the exit passageways 120 of the airfoil 100, exterior airfoil surfaces 102 near exit holes 122 of the exit passageways 120 can be heated by convective contact (FIG. 9D). Once the original static air volume has been expelled, it is replaced by a steady flow of relatively cooler pressurized air, which is expelled through the exit passageways 120 at a steady rate. The initial temperature rise that occurred as a result of gas compression is quenched by a combination of conduction of the heat generated at the exterior airfoil surface 102 into the interior of an associated airfoil wall 104 of the airfoil 100, and convective cooling by the subsequent steady state airflow.

Referring to FIGS. 7, 8A and 9E, the operations further include ceasing 806 delivery of the air flow to the airfoil 100, and then ceasing 808 image acquisition from the infrared camera 710. As the forced airflow is substantially instantaneously shut off, the airfoil 100 experiences a sudden decrease in internal air pressure, which results in a sudden temperature drop of the air and/or the airfoil 100. Air in the airfoil 100 may expand into the delivery line 750, resulting in a drop in internal airfoil pressure.

Referring again to FIG. 7, in some examples, the computing device 720 receives a continuous sequence of thermal images 712 of at least an exit hole 122 defined by the at least one internal passageway 120 at a surface 102 of the airfoil 100. The computing device also causes the air source 730 to deliver a pressurized airflow pulse into the at least one internal passageway 110, 120 of the airfoil 100 and to cease delivery of the airflow pulse. The computing device 720 determines a temperature response signal as function of time based on the received thermal images 712, a first derivative 1001 of the temperature response signal 1000, and a level of blockage of the at least one internal passageway 110, 120 based on the first derivative 1001 of the temperature response signal 1000.

Referring to FIGS. 7 and 10A-10C, the infrared camera 710 may capture images 712 of the airfoil 100 before, during, and after delivery of the air pulse. The net effect of the air delivery process on a surface temperature of the airfoil 100 in the immediate vicinity of an exit passageway hole 122 is a sudden temperature increase (e.g., due to expulsion of the original static gas), followed by a gradual temperature decrease (e.g., due to conduction and convection), and then a sudden temperature drop (e.g., due to decompression at shutoff). The entire process results in a predictable temperature response signal 1000 detectable by the infrared camera 710, as illustrated in FIGS. 10A-10C, providing exemplary graphs of a temperature response signal 1000 as a function of time. The surface temperature of the exterior airfoil surfaces 102 about an exit passageway hole 122 is at an equilibrium temperature with its environment during a first time period 1002 of the thermographic testing before any air delivery to the airfoil 100. During a second time period 1004, the surface temperature of the airfoil 100 in the immediate vicinity of the monitored exit passageway hole 122 suddenly increases due to compression of the static air inside the airfoil 100 and delivery of the compressed air pulse into the airfoil 100. The sudden temperature increase has a corresponding temperature peak 1012 on the temperature response signal 1000. The compression air is then expelled through the corresponding exit passageway 120. During a third time period 1006, the airfoil 100 experiences a gradual temperature decrease due to conduction and convection of the heat generated by initially compressing the air inside the airfoil passageways 110, 120. As a result, a portion 1014 of the temperature response signal 1000 corresponds to a gradual temperature decrease or a negative slope. Upon ceasing delivery of the air flow into the airfoil 100, the airfoil 100 experiences a sudden temperature drop during a fourth time period 1008, due to decompression and expansion of the air inside of the airfoil 100. The sudden temperature decrease has a corresponding temperature dip 1016 on the temperature response signal 1000. During a fifth time period 1010, the temperature of the airfoil 100 and the air inside the airfoil 100 return to equilibrium with the surrounding environment.

Referring to FIGS. 7-10C, the testing operations further include determining 810 a time derivative of a temperature response signal 1000 as a function of time and evaluating 812 a pixel time history for each pixel 714 of the camera 710 to identify positive and negative derivative peaks corresponding to the sudden temperature increase and subsequent sudden decrease. With additional reference to FIGS. 17A and 17B, the operations may include identifying image pixels 714 corresponding to positive derivative peaks 1007 immediately followed by negative derivative peaks 1009 for identifying the temperature peak 1012 corresponding to the onset of the forced air pulse, where both derivative peaks 1007, 1009 are greater than a threshold peak value A. The threshold peak value A can be determined separately for each test component or a standard value can be used. The operations can also include identifying image pixels 714 corresponding to negative derivative peaks 1009 immediately followed by positive derivative peaks 1007 for identifying the temperature dip 1016 corresponding to the shut-off of the forced air pulse, where both derivative peaks 1007, 1009 are greater than a threshold peak value B. This threshold peak value B can be the same as or different from the threshold peak value A used for identifying the temperature peak 1012. Moreover, the threshold peak values A, B may be determined by evaluating a statistically significant number of components (e.g., air foils 100) that have been determined to be blocked or unblocked by other methods. In some implementations, the operations include identify pixels 714 where the difference between the positive and negative derivative peaks for onset and shut-off is greater than a threshold peak difference C.

The operations may optionally include determining that the derivative signal 1001 of the image pixels 714 returns to equilibrium (e.g., within an equilibrium threshold range of zero) within a threshold number of frames for identifying the fifth time period 1010 of the temperature response signal.

While the actual, or even the relative temperature of exterior airfoil surfaces 102 near an exit passageway hole 122 or near a given exit may depend on many factors, including the static and forced air temperature, airfoil temperature, pressure of the forced air, the overall graphical shape of a temperature response signal 1000 as a function of time is largely independent of these factors. An airfoil area 106 (FIG. 9E) in and immediately surrounding an unobstructed exit passageway hole 122 will display a sharp positive temperature peak 1012 substantially immediately after the onset of delivery of the air pulse, and a corresponding negative peak 1016 substantially immediately after cessation of delivery of the air flow, with a substantially gradual temperature decrease portion 1014 therebetween. In contrast, the same points on a temperature response signal 1000 b associated with a blocked hole 122 will display a relatively more gradual heating and cooling curve, as illustrated in the example shown in FIG. 10C.

Referring to FIG. 10D, the operations may include associating the identified image pixels 714 having a temperature response signal 1000 satisfying the pixel time history evaluation with unblocked holes 122 and optionally marking corresponding pixel locations on a digital image 712 from the infrared camera 710. An operator may view the digital image 712 and/or the temperature response signals to determine which exit passageways 120 are open and which passageways 120 are blocked or partially blocked.

Referring again to FIG. 10C, in some implementations, a method of determining a level of blockage of an internal passageway 110, 120 includes analyzing a shapes of the temperature response signals 1000 (i.e., a signal derived from a temperature-time history of a corresponding pixel 714 of a sequence of thermal images 712 captured by the infrared camera 710) for each pixel 714 of the thermal images 712. In the example shown, the temperature response signals 1000 includes a first peak 1012 having a relatively fast rise time (i.e., a rapid increase in temperature over a relatively short period of time). The method may include identifying a first peak 1012 having monotonically rising shape or portion 1020 and/or a threshold change in temperature during a time period occurring between an onset of the air pulse and a time at which the corresponding pixel 714 attains a maximum temperature over the sampled period of time.

Similarly, the method may include identify a second peak 1016 having monotonically falling shape or portion 1030 and/or a threshold change in temperature during a time period occurring between shut-off time of the air pulse and a time at which the corresponding pixel 714 attains a minimum temperature over the sampled period of time. The method may also include fitting a polynomial to at least one of the identified monotonical portions 1020, 1030 of the temperature response signal 1000 and/or comparing the temperature response signal 1000 to a reference signal 1000 corresponding to a validated component.

FIG. 11 illustrates an exemplary airfoil 100 having a wall 104 defining different types of exit holes 122. In the example shown, the airfoil wall 104 defines a straight through exit hole 122 a, where walls 124 of the corresponding exit passageway 120 are substantially normal (e.g. perpendicular) to the exterior airfoil surface 102. In some examples, the airfoil wall 104 defines an angled exit hole 122 b, where the walls 124 of the corresponding exit passageway 120 are arranged at an angle with respect to the exterior airfoil surface 102. This arrangement causes exiting air to pass over the exterior airfoil surface 102 promoting surface cooling. For example, the angled holes 122 b can direct a film 126 of air over the airfoil surface 102 to cause film cooling of the airfoil 100 through convection between the air film 126 and the airfoil surface 102. As a result, the angled holes 122 b may provide relatively more efficient cooling of the airfoil 100 than straight through holes 122 a. In yet further examples, the airfoil wall 104 defines a complex exit hole 122 c, where the walls 124 of the corresponding exit passageway 120 are arranged at different angles with respect to the exterior airfoil surface 102. The complex holes 122 c can be configured to cause exiting air to pass over the exterior airfoil surface 102 in multiple directions, thus creating air films 126 along multiple directions covering a relatively larger surface area than the other two types of holes 122 a, 122 b. Depending on a position of the infrared camera 710, the system 700 can measure infrared radiation emitted from the airfoil surface 102, walls 124 of an exit passageway 120, and walls 114, 124 of other internal passageways 110, 120, as shown in FIG. 12.

Referring to FIG. 13, the sudden introduction of a relatively high pressure gas stream into the fixed volume of the passageways 110, 120 of the airfoil 100 causes substantially instantaneous compression of the static air initially present in the passageways 110, 120, and thus heating of the air. The heated air heats the walls 114, 124 of at least some of the internal passageways 110, 120. The internal walls 114, 124 may consequently conduct heat from the internal passageways 110, 120 through the airfoil wall 104 to the exterior surface 102 of the airfoil 100. A transit time of the heat conduction may depend on a thickness and/or composition (e.g., thermal diffusivity) of the airfoil wall 104.

Referring to FIG. 14, in some implementations, an external surface 102 of the airfoil 100 (and/or a thermal barrier coating 130 thereon) receives a film 126 of expelled air from an angled exit hole 122 b, for example. The expelled air film 126, when heated, can heat the external airfoil surface 102 through convection (or the thermal barrier coating 130, which conducts heat to the airfoil wall 104). For example, at a point F on the exterior airfoil surface 102 near the exit hole 122, 122 b and in a path of the airflow, the expelled airflow can create an air film 126 across the exterior airfoil surface 102 (e.g., across the thermal barrier coating 130) which heats the exterior airfoil surface 102 through convection with the received airflow. Moreover, conduction of heat through the airfoil wall 104 can further heat the exterior airfoil surface 102.

Referring to FIGS. 15A and 15B, the airfoil 100 may experience different modes or paths of surface heating due to the heated internal air. In the example shown, the airfoil 100 can experience heating by conduction through the airfoil wall 104, internal core heating by conduction, film heating by convection of a received airflow out of an exit hole 122 and over the thermal barrier coating 130, and a combination of localized film heating and airfoil wall conduction near an exit hole 122. For a brief period of time after the introduction of compressed air into the airfoil 100, film heating of the exterior airfoil surface 102 (e.g., via) may dominate as the surface heating mode for the airfoil 100. After that brief period of time, other modes of surface heating may dominate.

FIGS. 16A and 16B illustrate an example where the shape of a heated air film 126 on the exterior airfoil surface 102 can be captured on a thermal image 712 (FIG. 16A) of a corresponding airfoil 100 (FIG. 16B). Initial escapement of the heated internal air may cause heating of the exterior airfoil surfaces 102 about the corresponding exit hole 122. Moreover, later steady state flow relatively cooler air may cause cooling of those exterior airfoil surfaces 102. Air escaping through an exit hole 120 can be directed by the exit hole 120 (e.g., by an angled exit hole 122 b or a complex exit hole 122 c) over the airfoil surface 102. The exit hole 122 can be arranged or configured (e.g., constructed with a specific shape) to create certain shape of air film 126 on the air foil surface 102, such as an elliptical shape, fan shape, or other suitable shape. The thermographic inspection of the airfoil 100 may include analysis of the shape of the thermally detected air film 126 (e.g., shape of air film in a thermal image 712) to determine whether the corresponding exit hole 122 meets a specification. For example, a detected thermal shape of the air film 126 can be compared against reference shapes of air films corresponding to properly formed exit holes 122. The inspection may include receiving or determining a temperature response signal 1000 as function of time for each corresponding pixel 714 of the received thermal images 712 from the infrared camera 710 and determining the shape of the air film 126 based on the temperature response signals 1000. During operation of the air foil 100, the expelled air film 126 acts to cool the hot airfoil surface 102. Since the inspection is performed on an air foil 100 at room temperature, the expelled air heats the air foil surface 102, thus allowing thermographic detection and inspection of the air film shape.

In some implementations, the operations for thermographically testing the airfoil 100 (or another component) includes delivering a sequence of several air pulses (e.g., 2-3) to confirm repeatability of the test results. The first air pulse can be used to help seat the airfoil 100 in a test fixture and may be discarded, as some motion of the airfoil 100 may occur due to the initial application of forced air. The airfoil 100 may be imaged by the infrared camera 710 either directly, or using mirrors, so that top, bottom, side, and/or edge surfaces of the airfoil 100 may be tested simultaneously. FIG. 17A provides exemplary graphs of a first temperature response signal 1000 a for an unblocked passageway 110, 120 and second temperature response signal 1000 b for a blocked passageway 110, 120. FIG. 17B provides exemplary first derivatives 1001 a, 1001 b of the temperature response signals 1000 a, 1000 b shown in FIG. 17A. In this example, the testing system 700 delivers two sequential air pulses to the airfoil 100, while acquiring thermal images 712 of the airfoil 100 using the infrared camera 710. Both air pulses result in repeatable temperature response signals 1000 a, 1000 b.

A degree of blockage of a passageway 110, 120 can be determined by comparison of a peak amplitude of the temperature response signal for a test airfoil 100 to a peak amplitude of the corresponding temperature response signal for the reference airfoil 100. In the example shown in FIG. 17A, the first temperature response signal 1000 a is of a reference airfoil 100 have known or otherwise verified unblocked passageways 110, 120, while the second temperature response signal 1000 b is of a test airfoil 100. Analysis of the corresponding first derivative signals 1001 a, 1001 b shown in FIG. 17B reveals that the first derivative signal 1001 b of the tested airfoil 100 has uni-polar onset and shut-off signal portions 1702, 1704 (i.e., has only a positive peak 1007 or a negative peak 1009), signifying a blocked passageway 110, 120. In contrast, the first derivative signal 1001 a of the reference airfoil 100, which has a bi-polar onset signal portion 1702 (i.e., has a positive peak 1007 followed by a negative peak 1009) and bi-polar shut-off signal 1704 (i.e., has a negative peak 1009 followed by a positive peak 1007) opposite in polarity from the onset signal portion. Moreover, the onset signal portion 1702 of the first derivative signal 1001 b of the tested airfoil 100 has a positive peak 1007 with an amplitude less than the threshold peak value A (e.g., 25) and no negative peak 1009 (e.g., has an amplitude less than the threshold peak value A). The positive and/or negative peak amplitudes can be used to determine a level of passageway blockage, for example, based on a one-to-one correspondence of amplitude to blockage or other relationships. The onset and shut-off signal portions 1702, 1704 occur during the corresponding second and fourth time periods 1004, 4008.

For evaluating 812 the pixel time histories, the threshold values A, B, N may be established using the reference airfoil 100 or a component having verified unblocked passageways 110, 120. Moreover, the operations can be performed automatically using an automated system. The testing operations can provide a quantitative analysis of the airfoil 100 to identify, evaluate, and quantify localized blockages. Rather than looking at a net air flow to determine that there is a blockage and not necessarily a location of the blockage does not allow for easy reworking or repair of a blocked airfoil 100. Since the testing method does not require control or monitoring of inlet or outlet temperatures or pressures, the testing system 700 can operate in just about any environment and does not require determining a net energy balance or precise control and monitoring of environmental or air parameters. Moreover, the testing method does not require precise radiometric temperature measurement. Instead, the testing method analyzes the shape of the temperature-time history of each image pixel 714, not necessarily the amplitude. As such, emissivity of the airfoil may vary, or be less than ideal.

In some implementations, the testing system 700 can be used to determine blockage levels of internal structures and/or inspect internal cavities that have no exit holes on the airfoil surface and terminate within the airfoil 100. While much of the compressed heated air escapes through passageways 120 having exit holes 122, the heated air may convectively heat other internal structures of the air foil 100. As illustrated in FIG. 16B, heat from the heated internal walls 114, 124 of the airfoil 100 may propagate toward the cooler exterior surface 102 of the airfoil 100 through the process of thermal diffusion. The time required for heat to diffuse through the airfoil 100 wall can be determined by the thermal diffusivity of the material (e.g., metal or superalloy) of the airfoil 100 and a local thickness T of the airfoil wall 104.

The process of compressed air heating causing thermal diffusion through the airfoil wall 104, and ultimately, a temperature rise at the exterior surface 102 of the airfoil 100 allows determination of changes in airfoil wall thickness T. In examples where the airfoil 100 comprises a homogeneous superalloy structure, thermal diffusivity may be relatively constant throughout the airfoil 100. Moreover, changes in a propagation time for heat to diffuse through the airfoil wall 104 and/or a maximum amplitude of the airfoil surface temperature can be associated with changes in airfoil wall thickness T along the airfoil 100.

FIG. 18A provides exemplary temperature response signals 1000 a, 1000 b, 1000 c for an unblocked hole 122, a blocked hole 122, and a solid exterior airfoil surface 102, respectively. The corresponding data was collected from the infrared camera 710 operating at a frame rate of about 500 Hz for a capture time of about 1 second for an airflow duration of about 500 milliseconds having an airflow start pressure of about 128 psi. FIG. 18B illustrates the second time period 1004 of the temperature response signals 1000 a, 1000 b, 1000 c shown in FIG. 18A, which occurs during a compression-dominated time regime. During and immediately after the second time period 1004, the compression heating period, the surface temperature of the airfoil 100 monotonically rises. In the example shown, the monotonic temperature rise occurs over a period of about 14 milliseconds during the compression heating period or the second time period 1004. The monotonically rising portion 1020 of the temperature response signal 1000 can be enhanced by thermographic signal reconstruction (TSR), a process which allows viewing of noise free time derivatives of the temperature response signal 1000 and significantly enhances the sensitivity of the temperature response signal 1000 to small changes. Details on the TSR process and other features combinable with this disclosure can be found in U.S. patent application Ser. No. 10/848,274, filed on May 18, 2004 (issued as U.S. Pat. No. 7,724,925), which is hereby incorporated by reference in its entirety. After the second period 1004, the surface temperature falls during the third time period 1006 due to the steady state airflow through the internal passageways 110, 120 and convective cooling of the exterior airfoil surface 102 by the exterior environment (which may be at room temperature). The entire process can be observed and recorded with the infrared camera 710 (FIG. 7) operating at a frame rate sufficiently fast to capture the transient heating period (e.g., at a frame rate of at least 150 Hz).

Infrared images 712 of the airfoil 100 captured during thermographic testing can be analyzed for determining changes in airfoil wall thickness T. Attachments to the internal cooling channel wall which effectively change the wall thickness, e.g. pillars, posts or turbulators, all designed to control convective airflow in the cooling channels, may appear in the infrared images 712 during this period. Examination of the images 712 may be performed to confirm that these structures were cast properly.

FIG. 19 provides an exemplary arrangement 1900 of operations for determining a local wall thickness T of an object having internal channels, such as an airfoil 100. The operations include receiving 1902 a a thermal diffusivity of the object or receiving 1902 b a non-local wall thickness T of the object. The operations also include receiving 1904 a temperature response signal 1000 (temperature change as a function of time) for the object and fitting 1906 a mathematical expression to the temperature response signal 1000 for one-dimensional diffusion through a plate subjected to instantaneous, uniform heating, with thickness as the free parameter. The mathematical expression may be fit to the monotonically rising portion 1020 of the temperature response signal 1000. The operations include determining 1908 a wall thickness T of the tested object. In some examples, the operations include determining a temperature response signal 100 for each corresponding pixel 714 of the received sequence of thermal images 712, identifying a location on the thermal images 712 of the at least one internal channel and determining a wall thickness between the at least one internal channel and a surface of the object.

Alternatively, the method may include measuring the time at which each point on a surface of the object (e.g., the airfoil surface 104) reaches half of its maximum temperature and determining a local wall thickness T according to:

$\begin{matrix} {\alpha = \frac{\left( {0.139*T^{2}} \right)}{t_{half}}} & (1) \end{matrix}$

where α is thermal diffusivity, T is the wall thickness, t_(half) is the time at which the temperature response signal 1000 reaches ½ of its maximum amplitude.

Wall thickness determinations along can be used to confirm proper formation of internal structures of the object. For example, the wall thickness determination method can be used to verify proper casting (e.g., that the ceramic cores did not shift position during the casting process), machining, and coating of the airfoil 100 at each step of the manufacturing process. Moreover, the method can be used to determine partial or complete blockage of the internal structures.

Referring again to FIG. 6, debris 300, such as remnants of the ceramic core used to form the internal passageways 110, 120 during the casting process, may remain in the passageways 110, 120 after the chemical leaching process. The residual core may occur in many forms, e.g. pieces that become lodged in the passageways 110, 120 or exit holes 122, a layer that becomes attached to a passageway wall 114, 124, or small isolated spots that attach to the passageway wall 114, 124. The debris 300 (e.g., residual core) may affect the cooling function of the airfoil 100. For example, core debris 300 blocking or narrowing the passageways 110, 120 can reduce the cooling airflow through the passageways 110, 120. Moreover, layers of debris 300 attached to a passageway wall 114, 124 may compromise the convective heat transfer of the cooling air flow during operation of the airfoil 100.

The presence of debris 300 in any of the airfoil passageways 110, 120 can be detected using infrared themography. In some implementations, the temperature response signal 1000 of the airfoil 100 includes identifiable qualities (e.g., peaks, shapes, etc.) for discerning a level of internal passageway blockage. For example, a discontinuity in the second time period 1004 of a temperature response signal 1000 during a compression-dominated time regime for image pixels 714 of the airfoil surface 102 substantially directly above an internal passageway 110, 120 can be characteristic of a blockage or constriction of that passageway 110, 120.

Thermographic signal reconstruction (TSR) operates on a reconstructed representation of the entire temperature-time history of acquired thermographic data rather than the raw thermographic data. This approach is beneficial because:

A. The reconstructed representation of the temperature-time history of the thermographic data is typically an order of magnitude smaller than the raw thermographic data in terms of the amount of computer memory it requires for storage.

B. The reconstructed representation of the thermographic data is almost entirely free of temporal noise (typically introduced from the infrared camera) and mechanical instability.

C. The reconstructed representation of the thermographic data can be based on an analysis of derivatives (rather than contrast relative to nearby points) of the time evolution of each point in the image 712. Analyzing derivatives lends itself to directly automating the image inspection task because they can be objectively analyzed for characteristic features (zero crossings, extrema, etc.) without visual confirmation by an operator.

D. The inspection requires no a priori knowledge of the physical characteristics of the sample. A priori knowledge is not necessary because the nature of the reconstructed representation of the thermographic image 712 (taken from defect free samples) differs only in scale from sample to sample (there is no deviation in shape from sample to sample).

E. The inspection can be based on a well-known physical model that allows analysis of a sample response to excitation as a deterministic phenomenon and not a phenomenon which is linked to thermographic data collected from neighboring points.

Referring to FIG. 20, a method 200 of thermographic signal reconstruction first involves starting 202 acquisition of a sequence of infrared images 712 from the sample and then thermally exciting 204 the sample (e.g., by introducing a compressed air pulse into an internal channel of the sample). The image sequence can be stored in computer memory, videotape, or any other electronic storage means. The acquisition process is terminated 206 after a predetermined time and digital data corresponding to the image sequence is transferred 208 to a computing device 720 or dedicated hardware for mathematical analysis.

If the data is in analog format, the method includes digitizing 210 the data. The length of the image sequence will depend on the type of material being inspected and the thickness of the component. If the material has low thermal conductivity and/or if the component is relatively this, the image sequence may be lengthened. A typical image sequence from an infrared camera 710 operating at 60 frames per second will contain several hundred frames. In extreme cases, the image sequence may contain as many as several thousands of frames. The time over which the data acquisition step 201 takes place can range over several seconds as the sample temperature returns to equilibrium, but the specific length of time will vary depending on the thermal properties of the sample. Further, the output image sequence (or defect map sequence) can be generated over any time duration bounded between the heating flash event and the last image sequence acquisition event, independent of the sampling rate of the infrared camera 710.

The method includes subtracting 212 the pre-excitation temperature amplitude of each pixel 714 from the post-excitation history temperature for that pixel 714. The process is applied to every pixel 714 in the field of view of every image 712 in the image sequence. The result of subtracting the pre-excitation temperature is that the resulting signal indicates the sample's response to the thermal excitation event and negates any influence that the sample's ambient temperature prior to excitation might otherwise have on the data.

The method may optionally include smoothing 214 the subtracted data using any number of smoothing techniques. Smoothing is necessary because although the overall trend of the surface temperature of the sample is monotonically decreasing, consecutive data points in the post-excitation time history may not behave as expected due to undesirable noise artifacts. These undesirable noise artifacts typically are composed of high frequency components and are easily removed by fitting a straight line segment (or second order polynomial) to groups of adjacent points and replacing a particular point with the value on the straight line. This process can be repeated for every point in the time history; however, the number of points chosen in each grouping should increase as the latter occurring data is smoothed. This allows each line segment to become longer as later points occurring later in the time history are smoothed. This approach accurately models the later occurring data primarily because as time extends further away from the onset of the thermal pulse, the image data tends to change less than it did earlier in time and accordingly behaves more linear.

The method may include executing 216 flash timing compensation. This may be necessary because the one-dimensional heat flow model used as a theoretical basis in this application, assumes that the sample is heated instantaneously by a heat pulse which is infinitesimal, and that this heat pulse occurs at time=t_(o). In practice, the duration of the heat pulse is finite, and may occur between video frames. The result is a deviation from linearity in the earliest data points in the post-flash time history. By subtracting a time increment from every pixel 714 that is equivalent to the time delay difference between t=0 and the peak of the excitation signal, the early non-linearity is removed. This technique amounts to synchronizing the frame of the camera with the flash event of the heat pulse. If this non-linearity is not compensated for, it manifests itself in a “kink” in the graphical representation of the early segment of the data.

The method further includes scaling 218 the data. The data may be scaled in a way which reduces the dynamic range of the post-flash time history and causes it to behave in a linear, or near linear, manner if no sub-surface defects are present. One such scaling operation entails using the natural logarithm of the temperature versus natural logarithm of time plot (see FIG. 21A of prescaled data and FIG. 21B of post scaled data). This approach results in a temperature versus time plot of a defect free sample as a straight line with a slope of −0.5 (the slope is the same irrespective of the sample composition or hardware used in the imaging process). However, other scaling operations are possible. For example, scaling by using the inverse square of the temperature (T⁻²) versus time results in an ascending straight-line result for a defect-free sample. In either case, the behavior follows the predictions of a one-dimensional solution of the heat diffusion equation.

The method includes interpolating 220 data. The post-excitation response of the sample is governed by diffusion of heat into the sample and this diffusion of heat can be described by a diffusion equation. As a result, the surface temperature changes rapidly immediately after excitation, but the rate of change decreases as time progresses (see FIG. 21A). If data is acquired at a constant (frame) rate, the abrupt decay occurring in the early stages of the sample cool down causes there to be too few early time data points and an excessive number of later data points (this is clearly seen in the plot of temperature decay versus time of FIG. 21A). A more accurate way to model the true thermal behavior of the sample is to add reconstructed points by interpolation between early raw data points in order to increase the influences of early behavior in the fit. Also, improved fidelity to the underlying data is achieved if latter data points are sampled in a way which reduces the influence of the latter occurring data points (typically this is accomplished by thinning later occurring data points).

The method may include fitting 222 the data generated in the interpolating step 220 using a low order polynomial (preferably sixth order or less) using a least squares fit technique. Note that the disclosed method fits a polynomial to the natural logarithm of the temperature-time data and not to the actual (raw) temperature-time data in the linear domain. The low order polynomial serves as a low pass filter to ensure that only the information content of the data representing the thermal response of the sample is preserved and that the noise content of the data is rejected. The use of as low order polynomial as possible is counter intuitive but nonetheless it is the preferred method. Generally speaking, a higher order polynomial will allow you to fit the data with less error. However, because the source of the data is a thermal event (which are low frequency events), any high frequency information contained in the data can be confidently dismissed as noise and such high frequency noise can be easily filtered out using the lowest order polynomial which still permits reasonable fidelity to the underlying thermal information contained in the data. The resulting function for the amplitude for a given pixel at location i, j (i=row, j=column) is defined as:

ln[I _(ij)(t)]=a ₀ +a ₁ ln(t)+a ₂ [ln(t)]² + . . . +a _(n) [ln(t)]^(n)  (2)

The method includes inverted scaling 224 of the data to create a reconstructed version of the new data. Specifically, the inverse of the operation used in scaling step 218 to scale the data can be performed on the polynomial representation of the time history created in the fitting step 222. Accordingly, if we scaled the data using natural log scaling, we would invert the process by operating on the data using the following formula:

$\begin{matrix} \begin{matrix} {{I_{ij}(t)} = {\exp \left\{ {\ln\left( \left\lbrack {I_{ij}(t)} \right\rbrack \right\}} \right.}} \\ {= {\exp \left\{ \left\lbrack {a_{0} + {a_{1}{\ln (t)}} + {a_{2}\left\lbrack {\ln (t)} \right\rbrack}^{2} + \ldots + {a_{n}\left\lbrack {\ln (t)} \right\rbrack}^{n}} \right\} \right.}} \end{matrix} & (3) \end{matrix}$

Likewise if we scaled the data using the T² operation, we conduct an inverse operation to invert the T² operation.

As can be seen from equation 1, the polynomial resulting from fitting step 222 is a continuous function obtained from the discrete data, and thereby allows the method of the present invention to generate pixel amplitude values for all time values (even for time values that fall between frame acquisitions). Once the polynomial has been generated in fitting step 222 for each pixel 714, each pixel 714 is represented by an array of n polynomial coefficients, which will typically be six coefficients or less making it unnecessary to thereafter store the actual data sequence which can be several hundreds or even several thousands of frames generated by the infrared camera. Because of the polynomial representation includes only an array of coefficients, and because the polynomial representation of the pixel temperature-time characteristic is independent of the length of the data sequence, the amount of data that must be stored for any given pixel 714 is tremendously reduced by the polynomial representation and accordingly, much simpler to manipulate mathematically than raw camera data. The resulting file size for storing the pixel data is independent of the number of images 712 taken by the camera 710, further reducing the memory needed to store or manipulate the image data. For example, in one embodiment, the file size is equal to the number of pixels 714 being imaged multiplied by the number of coefficients in the polynomial multiplied by the number of bytes per coefficient, regardless of the number of images 712. The result of transforming the polynomial function from the logarithmic domain back to the linear domain, is a reconstructed temperature-time curve that has a significantly higher signal-to-noise ratio than the original raw signal, making it more suitable for signal analysis.

The method also includes determining 226 if any sub-surface defects are present in the reconstructed data. This determination can be done in any number of ways. Firstly, the reconstructed data for each pixel 714 can be assembled into an image which is displayed graphically to a user. Such an image is known as a defect map and an example is depicted in FIG. 22A. FIG. 22A is a front view of a control sample which has a plurality of flat bottom holes drilled into the sample from the back side. The holes are drilled at various depths (none of which pass through the sample) and accordingly manifest themselves in a reconstructed image as circular elements of various light intensities. These bright spots are also called “hot spots”.

FIG. 22B is a depiction of the same sample shown in FIG. 22A; however, the depiction in FIG. 22B is constructed from raw thermographic image data wherein the image of FIG. 22A is assembled using reconstructed thermographic image data derived from the process described in FIG. 20. Rather than simply visually analyzing the reconstructed data, in some applications it is far more convenient to examine the first, second, and even third time derivatives of the reconstructed data.

More particularly, if the reconstructed data is represented as:

$\begin{matrix} {{f(t)} = {\exp \left\lbrack {\sum{a_{1}\left\lbrack {\ln \; \underset{i = 0}{\overset{N}{(t)}}} \right\rbrack}^{i}} \right\rbrack}} & (4) \end{matrix}$

the first derivative can be expressed as:

f′(t)=t ⁻¹ [Σia _(i) [ln(t)]^(i-1) ]f(t)  (5)

and the second derivative can be expressed as:

f″(t)=t ⁻¹ [Σia ₁ [ln(t)]^(i-1)]² f(t)+f ⁻² {[Σi(i−1)a _(i) [ln(t)]^(i-2) ]−[ia _(i) [ln(t)]^(i-1) ]}f(t)  (6)

Images of the first and second derivatives (and other higher order derivatives) can be generated from Equations 5 and 6 through any means, if desired, by entering time information into the polynomial or its derivatives. Note that because the derivatives of the image data are calculated analytically rather than by fitting a straight line to the tangent of the noisy image data, the results obtained from the calculated derivatives yields more accurate results than attempts to compute the average over many noisy data points. Further, analytical calculation of the derivatives yields results that are true instantaneous derivatives rather than differentials over an interval spanning several image frames.

Also note that it is not necessary to convert the expressions back to their graphical format in order to glean useful information therefrom, it is sufficient to isolate and manipulate the arguments from expressions (4) and (5) to yield valuable information.

Because the method focuses on differentiating and analyzing the polynomial function instead of the raw image data, obtaining information about the thermal characteristics of the sample is much simpler because differentiating the polynomial representation is less computationally complex than differentiating a noisy signal. More particularly, operating on the coefficients of the polynomial, and not on the original data, eliminates the need to manipulate hundreds or even thousands of separate images, greatly improving the speed in which the image data can be analyzed. Also, because the first and second derivatives are obtained by manipulating the polynomial expression rather than conducting linear regression or curve fitting, the derivatives do not themselves contribute any noise to the final result. Further, because the method uses noise-reduced, analytically differentiated data obtained from scaled data, the noise reduction provided by the method allows more accurate detection of deeper and weaker defects as well as large defects encompassing the entire field of view.

Once steps 201 through 226 have been conducted for every pixel 714 at a given time t, an image representation 227 of the behavior of the sample at that time can be scaled to match the dynamic range of the display device. This scaling operation can be conducting using any common statistical scaling algorithm.

The image 227 or images based on the polynomial and/or its derivatives can be displayed on an output device, such as on a computer display screen. The display screen can be one or more discrete points on the sample, a single reconstructed image at a selected time t (FIG. 22A) or a sequence of reconstructed images displayed as a movie (not shown). The temporal resolution of the movie can be different than the actual data acquisition frame rate, if desired, to show the changes in the sample temperature more clearly; this can be conducted easily because the derived polynomial is a continuous function, as noted above.

Referring again to FIGS. 18A, 18B, 23 and 24, in some implementations, a method 2400 of detecting partial or relatively small blockages (e.g., core residue forming a coating along the passageway wall 114, 124) includes performing 2402 the TSR process on the monotonically rising portion 1020 of the temperature response signal 1000 for every pixel 714 during a time interval starting with the onset of air compression within the airfoil 100 and ending at a time when the corresponding pixel 714 attains its maximum temperature. The method includes determining 2404 a first derivative 1001 or second derivative of the TSR processed temperature response signal 1000 and identifying 2406 a location of one or more internal passageways 110, 120. The method further includes applying 2408 a line segment 230 along each identified passageway in a TSR image 227 and comparing 2410 the first derivative 1001 or the second derivative of the TSR processed temperature response signal 1000 at a particular time, or the maximum value of the derivative, for every point along the path segment 230 with one or more reference signals 1005 (e.g., first derivative 1001 of the TSR processed temperature response signal 1000 for reference airfoils 100 or components having verified unblocked passageways 110, 120 and exit holes 122). Since temperature response signals 1000 derived from corresponding pixels 714 of a series of thermal images iteratively captured from the infrared camera 710 at a specified frame rate can provide a time history of temperature change along an imaged airfoil surface 102 due to a received compressed air pulse, the process can be used to verify placement, size, arrangement, and/or level of blockage of internal structures (e.g., internal passageways 110, 120) of the airfoil 100.

The first or second derivative along a line segment 230 taken at a time (e.g., an optimal time) of a known clear (no residual core) component can be stored as a reference. Subsequent test components can be statistically correlated to the reference and automatically rejected if a correlation between the tested component and the reference component falls outside a set limit or range. A set of line segment locations and time settings for a particular airfoil 100 can be stored and recovered at a later time so that the identical test can be performed automatically on subsequent air foils 100 (e.g., of the same type). Instead of or in addition to comparing line segments 230, the entire derivative image of the airfoil 100 at the specified time can be compared to the reference.

Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, specially designed ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

These computer programs (also known as programs, software, software applications or code) include machine instructions for a programmable processor, and can be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. As used herein, the terms “machine-readable medium” and “computer-readable medium” refer to any computer program product, apparatus and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term “machine-readable signal” refers to any signal used to provide machine instructions and/or data to a programmable processor.

Implementations of the subject matter and the functional operations described in this specification can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Embodiments of the subject matter described in this specification can be implemented as one or more computer program products, i.e., one or more modules of computer program instructions encoded on a computer readable medium for execution by, or to control the operation of, data processing apparatus. The computer readable medium can be a machine-readable storage device, a machine-readable storage substrate, a memory device, a composition of matter effecting a machine-readable propagated signal, or a combination of one or more of them. The term “data processing apparatus” encompasses all apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers. The apparatus can include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them. A propagated signal is an artificially generated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal, that is generated to encode information for transmission to suitable receiver apparatus.

A computer program (also known as a program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program does not necessarily correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub programs, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.

The processes and logic flows described in this specification can be performed by one or more programmable processors executing one or more computer programs to perform functions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit).

Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read only memory or a random access memory or both. The essential elements of a computer are a processor for performing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto optical disks, or optical disks. However, a computer need not have such devices. Moreover, a computer can be embedded in another device, e.g., a mobile telephone, a personal digital assistant (PDA), a mobile audio player, a Global Positioning System (GPS) receiver, to name just a few. Computer readable media suitable for storing computer program instructions and data include all forms of non volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto optical disks; and CD ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.

Implementations of the subject matter described in this specification can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the subject matter described is this specification, or any combination of one or more such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (“LAN”) and a wide area network (“WAN”), e.g., the Internet.

The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

While this specification contains many specifics, these should not be construed as limitations on the scope of the invention or of what may be claimed, but rather as descriptions of features specific to particular implementations of the invention. Certain features that are described in this specification in the context of separate implementations can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.

Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multi-tasking and parallel processing may be advantageous. Moreover, the separation of various system components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

A number of implementations have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the disclosure. Accordingly, other implementations are within the scope of the following claims. For example, the actions recited in the claims can be performed in a different order and still achieve desirable results. 

1. A method of thermal inspection of a component defining at least one internal passageway, the method comprising: receiving a continuous sequence of thermal images of at least an exit hole defined by the at least one internal passageway at a surface of the component; delivering a pressurized airflow pulse into the at least one internal passageway; receiving a temperature response signal as function of time based on the received thermal images; determining a first derivative of the temperature response signal; and determining a level of blockage of the at least one internal passageway based on the first derivative of the temperature response signal.
 2. The method of claim 1, further comprising acquiring the thermal images at a frame rate of at least 150 Hz.
 3. The method of claim 1, further comprising comparing the first derivative of the temperature response signal of the tested component with a first derivative of a temperature response signal for a reference component having an unblocked corresponding at least one internal passageway.
 4. The method of claim 1, further comprising evaluating a pixel time history of the received thermal images to identify an airflow onset time period and an airflow shut-off time period.
 5. The method of claim 4, further comprising identifying peaks of the first derivative of the temperature response signal to determine the onset time period and the shut-off time period.
 6. The method of claim 5, further comprising identifying image pixels having a positive derivative peak immediately followed in time by a negative derivative peak as corresponding to the onset time period.
 7. The method of claim 5, further comprising identifying image pixels having a negative derivative peak immediately followed in time by a positive derivative peak as corresponding to the shut-off time period.
 8. The method of claim 1, further comprising identifying image pixels having a positive derivative peak immediately followed in time by a negative derivative peak, wherein both derivative peaks have an amplitude greater than a first threshold peak value.
 9. The method of claim 8, further comprising identifying image pixels having a negative derivative peak immediately followed in time by a positive derivative peak, wherein both derivative peaks have an amplitude greater than a second threshold peak value.
 10. The method of claim 9, wherein the first and second threshold peak values are equal.
 11. The method of claim 9, further comprising identifying image pixels wherein a difference between the identified positive derivative peaks and the negative derivative peaks for the onset and shut-off time periods is greater than a third threshold value.
 12. The method of claim 11, further comprising identifying image pixels having a first derivative of the temperature response signal that returns to within an equilibrium range of zero within a threshold number of image acquisition frames.
 13. The method of claim 11, further comprising associating an unblocked blockage level with image pixels evaluated as having: a positive derivative peak immediately followed in time by a negative derivative peak, wherein both derivative peaks have an amplitude greater than a first threshold peak value; and a negative derivative peak immediately followed in time by a positive derivative peak, wherein both derivative peaks have an amplitude greater than a second threshold peak value; wherein a difference between the identified positive derivative peaks and the negative derivative peaks for the onset and shut-off time periods is greater than a third threshold value.
 14. The method of claim 13, further comprising electronically displaying an image corresponding to the tested component, pixels of the image indicating a level of passageway blockage.
 15. The method of claim 14, wherein the image comprises a thermal image of the tested component.
 16. The method of claim 1, further comprising delivering a sequence of separate discrete pressurized airflow pulses into the at least one internal passageway.
 17. The method of claim 16, further comprising ignoring a portion of the temperature response signal corresponding to a first air pulse of the air pulse sequence.
 18. A method of thermal inspection of a component defining at least one internal passageway, the method comprising: receiving a continuous sequence of thermal images of at least an exit hole defined by the at least one internal passageway at a surface of the component; delivering a pressurized airflow pulse into the at least one internal passageway, the exit hole arranged to expel a film of air across the surface of the component; determining a shape of the air film based on at least one thermal image; and comparing the determined air film shape to a reference shape to determine whether the exit hole meets a specification.
 19. The method of claim 18, further comprising receiving a temperature response signal as function of time for each corresponding pixel of the received thermal images and determining the shape of the air film based on the temperature response signals.
 20. A method of thermal inspection of a component defining at least one internal passageway, the method comprising: receiving a continuous sequence of infrared images of a surface of the component; delivering a pressurized airflow pulse into the at least one internal passageway; receiving a temperature response signal as function of time based on the received thermal images; receiving a thermal diffusivity of the component; fitting a mathematical expression to the received temperature response signal; and determining a wall thickness of the component.
 21. The method of claim 20, wherein the mathematical expression comprises an expression for one-dimensional diffusion through a plate subjected to heating with a plate thickness as a free parameter.
 22. The method of claim 20, further comprising fitting the mathematical expression to a monotonically rising portion of the temperature response signal.
 23. The method of claim 20, further comprising acquiring the thermal images at a frame rate of at least 150 Hz.
 24. The method of claim 20, further comprising determining the temperature response signal for each corresponding pixel of the received thermal images.
 25. The method of claim 20, further comprising identifying a location on the thermal images of the at least one internal passageway and determining a wall thickness between the at least one internal passageway and a surface of the component.
 26. A method of thermal inspection of a component defining at least one internal passageway, the method comprising: receiving a continuous sequence of infrared images of a surface of the component; delivering a pressurized airflow pulse into the at least one internal passageway; receiving a temperature response signal as function of time based on the received thermal images; executing thermographic signal reconstruction on a monotonically rising portion of the temperature response signal occurring during a time interval starting at an onset of the air pulse delivery and ending at a time when the temperature response signal attains a maximum temperature; and comparing at least one of a first derivative of the reconstructed temperature response signal with a corresponding first derivative of a reconstructed reference temperature response signal and a second derivative of the reconstructed temperature response signal with a corresponding second derivative of the reconstructed reference temperature response signal to determine if the component meets a specification.
 27. The method of claim 26, wherein the thermographic signal reconstruction comprises: subtracting a pre-excitation signal portion from the temperature response signal; scaling the smoothed temperature response signal; interpolating between raw data points used for determining the temperature response signal; fitting a low order polynomial to the interpolated data; and executing inverted scaling of the data to provide a reconstruct data set.
 28. The method of claim 26, further comprising determining the first derivative of the reconstructed temperature response signal.
 29. The method of claim 26, further comprising: identifying a location of the at least one internal passageway on at least one infrared image; and comparing the first derivative of the reconstructed temperature response signal at one or more points coinciding with the identified location of the at least one internal passageway with the first derivative of the reconstructed reference temperature response signal to determine if the component meets the specification.
 30. The method of claim 26, further comprising: identifying a location of the at least one internal passageway on at least one infrared image; and comparing the second derivative of the reconstructed temperature response signal at one or more points coinciding with the identified location of the at least one internal passageway with the first derivative of the reconstructed reference temperature response signal to determine if the component meets the specification.
 31. The method of claim 26, further comprising storing in memory at least one of the first derivative of the reconstructed temperature response signal and the second derivative of the reconstructed temperature response signal.
 32. A method of thermal inspection of a component defining at least one internal passageway, the method comprising: receiving a continuous sequence of infrared images of a surface of the component; delivering a pressurized airflow pulse into the at least one internal passageway; receiving a temperature response signal as function of time based on the received thermal images; identifying a monotonically rising portion of the temperature response signal occurring during a time interval starting at an onset of the air pulse delivery and ending at a time when the temperature response signal attains a maximum temperature; and identifying a monotonically falling portion of the temperature response signal occurring during a time interval starting at cessation of the air pulse delivery and ending at a time when the temperature response signal reaches a minimum temperature.
 33. The method of claim 32, further comprising fitting a polynomial expression to at least one of the identified monotonical portions of the temperature response signal.
 34. The method of claim 32, further comprising comparing the temperature response signal to a reference temperature response signal corresponding to a validated component.
 35. A thermographic testing system comprising: a pressurized air source configured to receive fluid communication with a component having at least one internal passageway; an infrared camera arranged to capture images of the received component; and a computing device in communication with the infrared camera, the computing device: receiving a continuous sequence of thermal images of at least an exit hole defined by the at least one internal passageway at a surface of the component; causing the air source to deliver a pressurized airflow pulse into the at least one internal passageway of the component; determining a temperature response signal as function of time based on the received thermal images; determining a first derivative of the temperature response signal; and determining a level of blockage of the at least one internal passageway based on the first derivative of the temperature response signal.
 36. The thermographic testing system of claim 35, wherein the infrared camera operates with a frame rate of at least 150 Hz.
 37. The thermographic testing system of claim 35, wherein the computing device compares the first derivative of the temperature response signal of the tested component with a first derivative of a temperature response signal for a reference component having an unblocked corresponding at least one internal passageway.
 38. The thermographic testing system of claim 35, wherein the computing device evaluates a pixel time history of the received thermal images to identify an airflow onset time period and an airflow shut-off time period.
 39. The thermographic testing system of claim 38, wherein the computing device identifies peaks of the first derivative of the temperature response signal to determine the onset time period and the shut-off time period.
 40. The thermographic testing system of claim 39, wherein the computing device identifies image pixels having a positive derivative peak immediately followed in time by a negative derivative peak as corresponding to the onset time period.
 41. The thermographic testing system of claim 39, wherein the computing device identifies image pixels having a negative derivative peak immediately followed in time by a positive derivative peak as corresponding to the shut-off time period.
 42. The thermographic testing system of claim 35, wherein the computing device identifies image pixels having a positive derivative peak immediately followed in time by a negative derivative peak, wherein both derivative peaks have an amplitude greater than a first threshold peak value.
 43. The thermographic testing system of claim 42, wherein the computing device identifies image pixels having a negative derivative peak immediately followed in time by a positive derivative peak, wherein both derivative peaks have an amplitude greater than a second threshold peak value.
 44. The thermographic testing system of claim 43, wherein the first and second threshold peak values are equal.
 45. The thermographic testing system of claim 43, wherein the computing device identifies image pixels wherein a difference between the identified positive derivative peaks and the negative derivative peaks for the onset and shut-off time periods is greater than a third threshold value.
 46. The thermographic testing system of claim 45, wherein the computing device identifies image pixels having a first derivative of the temperature response signal that returns to within an equilibrium range of zero within a threshold number of image acquisition frames.
 47. The thermographic testing system of claim 45, wherein the computing device associates an unblocked blockage level with image pixels evaluated as having: a positive derivative peak immediately followed in time by a negative derivative peak, wherein both derivative peaks have an amplitude greater than a first threshold peak value; and a negative derivative peak immediately followed in time by a positive derivative peak, wherein both derivative peaks have an amplitude greater than a second threshold peak value; wherein a difference between the identified positive derivative peaks and the negative derivative peaks for the onset and shut-off time periods is greater than a third threshold value.
 48. The thermographic testing system of claim 47, wherein the computing device electronically displays an image corresponding to the tested component, pixels of the image indicating a level of passageway blockage.
 49. The thermographic testing system of claim 48, wherein the image comprises a thermal image of the tested component. 